Cover Page
The handle
http://hdl.handle.net/1887/137184
holds various files of this Leiden
University dissertation.
Author:
Esmeijer, K.
Title:
Risk factors of chronic kidney disease progression: Dutch cohort studies
Chapter 2 –
Cardiovascular risk factors accelerate
kidney function decline in
post-myocardial infarction patients:
The Alpha Omega Cohort study
Kevin Esmeijer, Johanna M. Geleijnse, Johan W. de Fijter, Erik J. Giltay, Daan Kromhout, Ellen K. Hoogeveen
ABSTRACT
Background: Impaired kidney function is a robust risk factor for cardiovascular mortality. The age-related annual kidney function decline after age 40y of 1.0
mL/min/1.73m2, is doubled in post-myocardial infarction (MI) patients. We
investigated the impact of the number of cardiovascular risk factors (including unhealthy lifestyle) on annual kidney function decline, in 2426 post-MI patients (60-80y) of the prospective Alpha Omega Cohort study.
Methods: Glomerular filtration rate was estimated by serum cystatin C
(eGFRcysC) and combined creatinine-cystatin C (eGFRcr-cysC), using the CKD-EPI
equations. Data were analysed by multivariable linear and logistic regression. Results: At baseline, mean (SD) eGFRcysC and eGFRcr-cysC were 81.5 (19.6) and 78.5
(18.7) mL/min/1.73m2, respectively. Of all patients, 79% were men, 19% had
diabetes, 56% had high blood pressure (≥140/90 mmHg), 16% were current smokers, 56% had high serum low-density lipoprotein (LDL ≥2.5 mmol/L),
and 23% were obese (body-mass index ≥30.0 kg/m2). After multivariable
adjustment, the additional annual eGFRcysC decline (95%-CI) was in patients
with vs without diabetes -0.90 (-1.23; -0.57) mL/min/1.73m2, in patients with
high vs normal blood pressure -0.50 (-0.76; -0.24), in obese vs non-obese patients -0.31 (-0.61; 0.01), and in current compared to non-smokers -0.19 (-0.54;
0.16) mL/min/1.73m2. High LDL was not associated with accelerated eGFR
cysC
decline. Similar results were obtained with eGFRcr-cysC.
Conclusions: In older stable post-MI patients without cardiovascular risk factors, the annual kidney function decline was -0.90 (-1.16; -0.65) mL/
min/1.73m2. In contrast, in post-MI patients with ≥3 cardiovascular risk factors,
the annual kidney function decline was 2.5-fold faster: -2.37 (-2.85; -1.89) mL/
INTRODUCTION
The incidence of chronic kidney disease (CKD) shows an increasing trend
worldwide.1 Impaired kidney function is a robust and independent risk factor
for cardiovascular and all-cause morbidity and mortality.2 In industrialized
countries, in healthy individuals after age 40y, kidney function gradually
declines annually about 0.8 to 1.0 mL/min/1.73m2.3, 4 In contrast, post-myocardial
infarction (MI) patients have an accelerated kidney function decline of about
2.2 mL/min/1.73m2 per year, and are thus more prone to develop CKD.5
Classic modifiable cardiovascular risk factors such as hypertension and
diabetes are important drivers for the development of CKD.6-10 The association
between elevated low-density lipoprotein (LDL) levels and kidney function
decline is less clear.11 Lifestyle factors, such as smoking of cigarettes and
adiposity, may increase the risk of hypertension and diabetes. All previous mentioned cardiovascular risk factors can have an unfavourable effect on kidney function owing to increased inflammation, oxidative stress, endothelial dysfunction, and disturbed coagulation. For example, accumulation of visceral adipose tissue may lead to increased production of inflammatory mediators by
adipocytes, which may contribute to glomerular and interstitial fibrosis.12, 13
Survival after MI has been improving, as a result of improved health care and pharmaceutical treatment. These trends, together with the global tendency towards a less healthy lifestyle and population aging, have resulted
in a considerable pool of patients at high risk for CKD.14 Little is known about
the beneficial effect of optimal treatment of cardiovascular risk factors and healthy life style on kidney function decline in post-MI patients. Since adequate drug-treatment of cardiovascular risk factors and modest lifestyle alterations are achievable and may retard kidney function decline in post-MI patients, we studied the association of modifiable cardiovascular risk factors (including lifestyle) in older stable post-MI patients of the Alpha Omega Cohort.
METHODS
Participants
This is a secondary analysis of the prospective Alpha Omega Cohort study (ClinicalTrials.gov no. NCT03192410). We included patients from the Alpha Omega Trial, a randomized controlled multi-center trial of omega-3 (n-3) fatty acids supplementation in 4837 patients with a verified history of MI. Patients were aged 60-80 years, and were receiving state-of-the-art antihypertensive, antithrombotic and lipid-modifying drug treatment, according to the
international guidelines, as described elsewhere.15, 16 The trial started in 2002
and ended in 2009. Patients with severe heart failure (NYHA stage IV) were excluded. For the present observational study, patients were selected from whom non-fasting blood was drawn at baseline and after 41 months. Owing to financial constraints two blood samples were available only for 2426 patients (50% of the cohort, i.e. those randomized before August 2005). Of all patients randomized prior to August 2005 (n=2918), 233 patients deceased during follow-up, and 259 patients had missing blood samples or refused participation (Supplementary Figure S1). This study was conducted in accordance with the Helsinki Declaration and was approved by a central medical ethics committee in the Netherlands. Written informed consent was obtained from all patients. Design and reporting of the current study was performed in accordance with
the STROBE Statement for cohort studies.17
Data collection
Patients were interviewed and physically examined by trained research nurses at baseline and after 41 months. Standardized blood handling procedures for
the Alpha Omega Trial are described in detail elsewhere.18, 19 Lipid, glucose
and high-sensitivity C-reactive protein (hsCRP) levels were determined as
described elsewhere.20 Information on demographic variables, lifestyle habits,
current health status, and medical history were collected by self-administered
questionnaires as previously described in detail.18 Questionnaires were checked
by research nurses. Information on smoking of cigarettes was obtained by self-reported questionnaires, and was dichotomized into current smoking vs non-smoking (former or never non-smoking). Alcohol consumption was dichotomized into at least 1 glass/week vs less than 1 glass/week. Systolic and diastolic
blood pressure (1st and 5th Korotkoff sound, respectively) were measured at the
according to the latest recommendations of the international guideline of the European Society of Cardiology: systolic blood pressure ≥140 mmHg or diastolic
blood pressure ≥90 mmHg.21 Diabetes mellitus was considered present in case
of a self-reported physician diagnosis, use of glucose-lowering drugs, and/or hyperglycemia. Hyperglycemia was defined as serum glucose ≥7.0 mmol/L for patients who had fasted 4 hours or ≥11.1 mmol/L for non-fasting patients. Serum
LDL was calculated using the Friedewald formula.22 High LDL was defined as
serum level ≥2.5 mmol/L.23 Body-mass index (BMI) was calculated as weight
in kilograms divided by the square of height in meters. Obesity was defined
as a BMI of ≥30.0 kg/m2, according to World Health Organization guidelines.24
Medication was coded according to the Anatomical Therapeutic Chemical (ATC) Classification System.
Kidney function assessment
At baseline and 41 months follow-up, serum cystatin C (cysC) was measured from stored blood samples in a central laboratory. We used calibrators and
assays of the same lot code, which was stable (no downward drift).25 Serum
creatinine (cr) was measured by the modified kinetic Jaffé method, as previously
described in detail.25 We estimated glomerular filtration rate based on cystatin C
(eGFRcysC) and combined creatinine-cystatin C (eGFRcr-cysC) at baseline and after
41 months, using the CKD-EPI equations from 2012, taking into account age, sex
and race.26 Both eGFR
cysC and eGFRcr-cysC are regarded superior measures of kidney
function compared to eGFR based on creatinine alone. In the main analyses we
use eGFRcysC as outcome, results for eGFRcr-cysC as outcome are reported in the
supplements. From each individual, eGFR decline was calculated by subtracting the eGFR at baseline from the eGFR after 41 months. Assuming a linear decline over time, we then estimated the annual kidney function decline. Rapid kidney
function decline was defined as an annual decline of >3 mL/min/1.73m2.27, 28
Data analysis
Baseline characteristics are presented for all patients and according to the number of cardiovascular risk factors. Baseline data are presented as mean (SD), median (interquartile range), or number (percentage) when appropriate. The following data were missing: LDL cholesterol (n=116), BMI (n=4), level of education (n=4), blood pressure (n=3), alcohol consumption (n=3). We accounted for missing data by multiple imputation, using five imputations, and including all relevant baseline variables and the outcome in the model.
We used analysis of covariance (ANCOVA) to compare annual eGFRcysC decline
rates for presence vs absence of a priori selected cardiovascular risk factors (including unhealthy lifestyle): diabetes, high blood pressure, high LDL levels,
current cigarette smoking, and obesity. In addition, we used multivariable logistic regression to estimate for each cardiovascular risk factor the risk of rapid kidney function decline. In all analyses, we used patients without the cardiovascular risk factor as the reference. All analyses are presented crude and adjusted for potential confounders: age, sex and three dummy variables for the four n-3-fatty acid treatment groups of the Alpha Omega Trial (model 1). In model 2 we adjusted in addition to model 1, for alcohol use (less than vs at least one glass/week), level of education (three dummy variables), and the five a priori selected cardiovascular risk factors.. Analyses for obesity were not adjusted for diabetes, high blood pressure and high LDL, because these factors are in the causal pathway between obesity and kidney function decline. Analyses were not adjusted for baseline eGFR, because baseline-adjustment in models with
change-scores as dependent variable results in biased and inflated estimates.29
We explored the presence of effect modification between age or sex and the modifiable risk factors with regard to kidney function decline by including interaction terms in our linear regression models. Furthermore, we repeated
analyses in strata of baseline eGFR (eGFR <60, 60<90, ≥90 mL/min/1.73m2).
Finally, we calculated the rate of kidney function decline and risk of rapid kidney function decline according to the number of cardiovascular risk factors present in each patient. In these analyses we included diabetes, high blood pressure, current smoking, and obesity. High LDL was excluded because of
lack of evidence that modifying LDL level affects cardiovascular risk.30 Patients
without cardiovascular risk factors have by definition an optimal cardiovascular risk profile and a healthy lifestyle, e.g. are considered being optimally treated for the included risk factors according to the latest guidelines of the European Society of Cardiology: blood pressure <140/90 mmHg, no diabetes, never smoked
cigarettes or ceased smoking, and no obesity (BMI<30 kg/m2).31 A linear trend
was evaluated by including a variable representing number of cardiovascular risk factors into the linear regression model.
Sensitivity analyses
We repeated all analyses without multiple imputation, using a complete case analysis. Next, we repeated the analyses adjusting for continuous instead of dichotomized variables, e.g. for BMI instead of obesity, and for systolic blood pressure instead of high blood pressure. Main analyses were repeated after adjustment for time since MI, hsCRP levels or use of RAS blocking drugs. We repeated the analyses in patients persistently (at baseline and after 41 months of follow-up) using RAS blocking drugs. Finally, we repeated the main analyses
using eGFRcr-cysC as outcome. In these analyses we excluded 82 patients of
disturbance.25 We considered two-sided P-values <0.05 statistically significant. All analyses were performed using SPSS 23.0 (SPSS, Inc., Chicago, IL).
RESULTS
Baseline characteristics
Baseline characteristics of all patients (n=2426), and stratified for the number of cardiovascular and lifestyle risk factors, are presented in Table 1. The mean (SD) age of the total study cohort was 68.9 (5.4) years, 79.4% were men, median
time since MI was 4.0 years, mean (SD) eGFRcysC and eGFRcr-cysC were 81.5 (19.6)
and 78.5 (18.7) mL/min/1.73m2, respectively. Of all patients, 23% were obese, 16%
were current smokers, 67% were former smokers, 44% had a blood pressure within the target range, 87% used anti-hypertensive medication, 54% used RAS blocking drugs of whom 92% persisted on RAS blocking drugs, and 19% had diabetes of whom 71% used glucose lowering medication. Finally, 44% of patients had normal LDL, 85% used statins, of whom 95% persisted on statins. Of all patients with high LDL (n=990) at baseline, 10% started with a statin during follow-up.
Kidney function decline
After 41 months of follow-up mean [95%-confidence interval (CI)] eGFRcysC decline
for all patients was -4.62 (-5.06; -4.18) mL/min/1.73m2, corresponding to an annual
decline of -1.34 (-1.47; -1.21) mL/min/1.73m2. Men and women had annual kidney
function decline rates of -1.45 (-1.59; -1.31) and -0.91 (-1.19; -0.63) mL/min/1.73m2,
respectively. Patients younger than 70 years vs 70 years or older had annual kidney
function decline rates of -1.15 (-1.32; -0.98) and -1.60 (-1.80; -1.41) mL/min/1.73m2,
respectively. After multivariable adjustment (model 2), patients without or with
diabetes had an annual eGFRcysC decline of -1.17 (-1.31; -1.03) and -2.07 (-2.37; -1.78)
mL/min/1.73m2 [difference -0.90 (-1.23; -0.57)] (Table 2). Patients with normal
or high blood pressure had annual decline rates of -1.01 (-1.20; -0.82) and -1.51
(-1.69; -1.34) mL/min/1.73m2 [difference -0.50 (-0.76; -0.24)]. Successive quartiles
of systolic blood pressure (quartile ranges: 86.5 to 128.0; 128.5 to141.5; 142.0 to 156.5; 157.0 to 237.5 mmHg) showed a faster annual kidney function decline: -0.99 (-1.25;
-0.74), -1.10 (-1.34; -0.85), -1.45 (-1.70; -1.20) and -1.82 (-2.07; -1.57) mL/min/1.73m2,
respectively. Each 10 mmHg systolic blood pressure increment was associated with an extra annual kidney function decline of -0.17 (-0.23; -0.12, P<0.001) mL/
min/1.73m2. We found a weak U-shaped relation between diastolic blood pressure
and kidney function decline. Annual kidney function decline for patients in the lowest through the highest quartile was -1.33 (-1.59; -1.08), -1.15 (-1.40; -0.91), -1.32
(-1.58; -1.06) and -1.55 (-1.80; -1.30) mL/min/1.73m2 (quartile ranges: 44.0 to 73.5;
74.0 to 81.0; 81.5 to 88.0; 88.5 to 124.0 mmHg). We found no significant difference in the rate of annual kidney function decline between patients with high compared to normal LDL levels. Smokers of cigarettes compared to non-smokers had an
additional annual eGFRcysC decline of -0.19 (-0.54; 0.16) mL/min/1.73m2. Obesity was
associated with a 23% faster kidney function decline (Table 2). We found no evidence for effect modification between sex, age, strata of baseline kidney function and the
pre-specified cardiovascular risk factors with regard to eGFRcysC decline.
Of all patients, 573 (24.4%) had a rapid kidney function decline. Table 3 shows the odds ratios (OR) for rapid kidney function decline according the a priori selected cardiovascular risk factors. Especially, diabetes [OR 1.72 (1.36; 2.17)] and high blood pressure [OR 1.43 (1.18; 1.74)] were strongly associated with rapid kidney function decline. Associations for current smoking [OR 1.21 (0.94; 1.57)] and obesity [OR 1.15 (0.92; 1.45)] were weaker. High LDL was associated with slower kidney function decline [OR 0.80 (0.66; 0.98)]. Results were comparable when defining rapid kidney
function decline as >5 mL/min/1.73m2 per year (data not shown). Furthermore,
Table 3: Odds ratios (95%-CI) for risk of rapid eGFRcysC decline (>3 mL/min/1.73m2 per
year) in 2426 post-MI patients, for different cardiovascular risk factors.
Risk factor¶ Crude Model 1 Model 2
Diabetes 1.77 (1.41; 2.21)** 1.79 (1.43; 2.25)** 1.72 (1.36; 2.17)**
High blood pressure 1.48 (1.22; 1.79)** 1.41 (1.17; 1.72)** 1.43 (1.18; 1.74)**
High LDL 0.81 (0.67; 0.98)* 0.82 (0.68; 0.99)* 0.80 (0.66; 0.98)*
Current cigarette smoking 1.13 (0.88; 1.45) 1.23 (0.95; 1.58) 1.21 (0.94; 1.57) Obesity 1.09 (0.88; 1.36) 1.17 (0.93; 1.46) 1.15 (0.92; 1.45)
CI, confidence interval; LDL, low-density lipoprotein. * p<0.05, ** p<0.001
¶ Reference: absence of the risk factor of interest.
Diabetes was defined as self-reported diagnosis by a physician, use of glucose-lowering drugs, or hyperglycemia. High blood pressure was defined as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg, irrespective of use of blood pressure lowering drugs. High
LDL was defined as serum LDL level ≥2.5 mmol/L. Obesity was defined as BMI ≥30.0 kg/m2.
Model 1: adjusted for age, sex and treatment group.
Model 2: Model 1 plus additional adjustment for current smoking, alcohol consumption, level of education, diabetes, high blood pressure, high LDL, and obesity. Analyses for obesity were not adjusted for diabetes, high blood pressure, and high LDL.
Kidney function decline and number of risk factors
We calculated the annual kidney function decline and risk for rapid kidney function decline in each patient according to the number of cardiovascular risk factors; diabetes, high blood pressure, current smoking of cigarettes and
obesity (BMI ≥ 30kg/m2). Patients without any of these cardiovascular risk
factors present (thus with optimal cardiovascular parameters and healthy lifestyle) had an annual kidney function decline of -0.90 (-1.16; -0.65) mL/
min/1.73m2 (Table 4, Figure 1). Each additional cardiovascular risk factor was
associated with a progressively faster annual kidney function decline [linear regression coefficient -0.45 (-0.59; -0.30) per additional risk factor, P for linear trend <0.001)]. Patients in whom three or more cardiovascular risk factors were present had an annual kidney function decline of -2.37 (-2.85; -1.89) mL/
min/1.73m2. Risk for rapid kidney function decline increased progressively with
every additional cardiovascular risk factor (Table 4, Figure 1). Patients with at least three cardiovascular risk factors had a 2.5-fold increased risk compared to patients without any cardiovascular risk factor. Patients in whom all four cardiovascular risk factors (n=16) were present, had an annual kidney function
Figure 1: Annual eGFRcysC decline according to the number of cardiovascular risk factors, in 2426 post-MI patients in the Alpha Omega cohort. For the four groups according to the number of
cardiovascular risk factors (diabetes, high blood pressure, obesity, and current smoking) the proportion (%) of the four different risk factors (columns, right vertical axis) and the mean (95%-CI) annual
eGFRcysC decline (black line, left vertical axis), adjusted for age, sex, and treatment group are presented.
SENSITIVITY ANALYSES
Results did not materially change when using complete cases only, instead of multiple imputed data. Adjustment with continuous instead of dichotomized variables did not change the results. Adjustment for time since MI, serum hsCRP, or use of RAS blocking drugs yielded similar results. Confining analyses to patients who persistently used RAS blocking drugs (n=1206), yielded comparable
associations. Analyses based on eGFRcr-cysC as outcome showed slightly weaker
effect estimates (Supplementary Table S1 and S2). The association between the
number of cardiovascular risk factors and annual eGFRcr-cysC or eGFRcysC decline
DISCUSSION
We showed in a cohort of stable older post-MI patients, that those patients with optimally treated cardiovascular risk factors, including healthy lifestyle, had an
annual kidney function decline of about -0.90 mL/min/1.73m2. In contrast, post-MI
patients with three or more suboptimal treated cardiovascular risk factors, had a
2.5-fold faster annual kidney function decline of about -2.37 mL/min/1.73m2. We
recently showed that in these post-MI patients, an eGFRcysC below 80 mL/min/1.73m2
is a graded risk factor for cardiovascular and all-cause mortality, underlining the
clinical relevance of these findings.2, 28
The mean annual kidney function decline of -0.90 (-1.16; -0.65) mL/min/1.73m2
that we found in optimally treated cardiac patients with healthy lifestyle, is within the normal range of the age-related kidney function decline in healthy individuals
of -1.0 mL/min/1.73m2.32-34 The mean (95%-CI) annual kidney function decline of
all patients was -1.34 (-1.47; -1.21) mL/min/1.73m2. Previously, the PREVEND study
reported in post-MI patients an annual kidney function decline (95%-CI) of -2.2
(-5.0; -0.9) mL/min/1.73m2.5 Post-MI patients of the PREVEND study had a similar
cardiovascular risk profile compared to patients of the Alpha Omega cohort. There are several explanations that may have resulted in the higher annual kidney function decline in the PREVEND compared to the Alpha Omega cohort. First, the small number (n=66) of post-MI patients in the PREVEND study resulted in a wide 95%-confidence interval and as a consequence the effect-estimate is less precise. Patients from the Alpha Omega cohort participated in a trial for 41 months. Trial patients generally are healthier and more compliant compared to the general
population, a phenomenon known as volunteer bias.35 Finally, during follow-up
patients in the Alpha Omega cohort (2002 to 2009) have been more strictly controlled according to more recent secondary prevention guidelines, compared to patients in the PREVEND cohort (1997 to 2005). In updated guidelines there was especially more attention for patient education, lifestyle monitoring (e.g. smoking cessation, weight management), diabetes and high blood pressure management, more strict
lipid regulation and standard prescription of statins and ACE-inhibitors.36, 37
In agreement with other studies, we found that diabetes and high blood pressure
were strongly associated with accelerated kidney function decline.7-10 Diabetes may
lead to diabetic nephropathy; a complex disease characterized by hemodynamic, metabolic, and inflammatory changes, ultimately leading to progressive interstitial
fibrosis and glomerular damage.38 High blood pressure may cause increased
intraglomerular pressure, leading to endothelial dysfunction, loss of adequate
auto-regulation and eventually to progressive glomerular and interstitial fibrosis.39 We
found a weak association between high LDL level and slower kidney function decline, but this association may be distorted by the fact that 85% of all patients used statins.
Moreover, of all patients with high LDL at baseline, 10% started with a statin during follow-up. Our finding is in concordance with recent guidelines, stating that CKD patients ≥50y should be treated with a statin independent of lipid levels without
trying to reach a target level.40 This paradigm shift is caused by the lack of evidence
linking changes in lipid levels with actual cardiovascular risk and emerging evidence
showing pleiotropic effects of statins.30, 41, 42
In line with other studies, we found that obesity was associated with faster
kidney function decline.8, 43 Obesity promotes deterioration of kidney function
through cardiovascular risk factors, such as diabetes and hypertension, and is also associated with visceral fat accumulation and accompanying inflammation, leading
to glomerular and interstitial fibrosis.12, 13, 24 Furthermore, we found that smoking
of cigarettes was associated with kidney function decline, which is confirmed by
other studies. 44 However, the association of smoking and kidney function decline
was weaker than expected; and could be underestimated due to underreporting, so called information bias.
Our study has some limitations. First, the observational design prevents us from making causal inferences. Second, kidney function was estimated at only two time points and was not directly measured. Third, we had no information about proteinuria, an important independent predictor of kidney function. Therefore, we could not study the association between optimal treatment of cardiovascular risk factors and change of proteinuria. Fourth, about 17% of patients dropped out owing to missing samples, refused participation, or death. If anything, this may have resulted in underestimation of the associations that we found, since patients who dropped-out were most likely less healthy. Fifth, volunteer bias may be present, since we only included trial patients. However, since volunteering patients usually are more healthy, we expect this may have led to an underestimation of our results. Finally, we analyzed post-MI patients only, which may hamper generalizability of our results. Notably, the prevalence of cardiovascular disease shows an increasing trend worldwide, our cohort of patients therefore represents a growing patient group.
A major strength of this study is our large homogeneous population of post-MI patients, which provides a unique opportunity to study the course of kidney function
decline in these patients. Second, we used both eGFRcysC and eGFRcr-cysC as outcome,
currently the most accurate available methods to estimate kidney function.26, 28, 45
ACKNOWLEDGEMENTS
The Alpha Omega Cohort Study is registered at ClinicalTrials.gov (no. NCT03192410). Results from this study have been previously presented at the ERA-EDTA Congress in Madrid, June 2017 (poster 55-SP) and at the Dutch Nephrology Days (NND) March 2017.
DISCLOSURES
EH is a member of the Guideline Committee of the Dutch Federation of Nephrology. JG received research funding from Unilever R&D for epidemiological studies of dietary fatty acids and is a member of the Standing Committee on Nutrition of the Dutch Health Council, Working Group on Minerals of the European Food and Safety Authority, and Dutch Academy for Nutritional Sciences, and is a Fellow of the American Heart Association. DK received research funding from the Royal Netherlands Academy of Arts and Sciences and is Member of the Dutch Academy of Nutritional Sciences. KE, JF, and EG report that they have no disclosures.
FUNDING
Financial support was obtained from the Dutch Kidney Foundation (PV41; EH), the Dutch Heart Foundation (JG and DK) and the National Institutes of Health (JG and DK). The grant from the Dutch Kidney Foundation covered baseline and final examinations of kidney function. The grant from the Dutch Heart Foundation covered baseline examinations. The grant from the National Institutes of Health covered final examinations.
AUTHORS’ CONTRIBUTIONS
KE, JG, DK and EH contributed to conception and design of the manuscript. KE, JG, EG, DK and EH contributed to acquisition, analysis and interpretation, and drafted the manuscript. JF contributed to interpretation. All authors critically revised the manuscript, all gave final approval and all agreed to be accountable for all aspects of work, ensuring integrity and accuracy.
REFERENCES
1. El Nahas AM, Bello AK. Chronic kidney disease: the global challenge. Lancet. 2005; 365: 331-340.
2. Hoogeveen EK, Geleijnse JM, Giltay EJ, et al. Kidney function and specific mortality in 60-80 years old post-myocardial infarction patients: A 10-year follow-up study.
PLoS One. 2017; 12: e0171868.
3. Lindeman RD, Tobin J, Shock NW. Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc. 1985; 33: 278-285.
4. Rowe JW, Andres R, Tobin JD, et al. The effect of age on creatinine clearance in men: a cross-sectional and longitudinal study. J Gerontol. 1976; 31: 155-163.
5. Eijkelkamp WB, de Graeff PA, van Veldhuisen DJ, et al. Effect of first myocardial ischemic event on renal function. Am J Cardiol. 2007; 100: 7-12.
6. Halbesma N, Brantsma AH, Bakker SJ, et al. Gender differences in predictors of the decline of renal function in the general population. Kidney Int. 2008; 74: 505-512. 7. Hobeika L, Hunt KJ, Neely BA, et al. Comparison of the Rate of Renal Function
Decline in NonProteinuric Patients With and Without Diabetes. Am J Med Sci. 2015; 350: 447-452.
8. Obermayr RP, Temml C, Knechtelsdorfer M, et al. Predictors of new-onset decline in kidney function in a general middle-european population. Nephrol Dial Transplant. 2008; 23: 1265-1273.
9. Rifkin DE, Katz R, Chonchol M, et al. Blood pressure components and decline in kidney function in community-living older adults: the Cardiovascular Health Study.
Am J Hypertens. 2013; 26: 1037-1044.
10. Young JH, Klag MJ, Muntner P, et al. Blood pressure and decline in kidney function: findings from the Systolic Hypertension in the Elderly Program (SHEP). J Am Soc
Nephrol. 2002; 13: 2776-2782.
11. Schaeffner ES, Kurth T, Curhan GC, et al. Cholesterol and the risk of renal dysfunction in apparently healthy men. J Am Soc Nephrol. 2003; 14: 2084-2091.
12. Bastard JP, Maachi M, Lagathu C, et al. Recent advances in the relationship between obesity, inflammation, and insulin resistance. Eur Cytokine Netw. 2006; 17: 4-12. 13. Bavbek N, Isik B, Kargili A, et al. Association of obesity with inflammation in occult
chronic kidney disease. J Nephrol. 2008; 21: 761-767.
14. Rosamond WD, Chambless LE, Heiss G, et al. Twenty-Two Year Trends in Incidence of Myocardial Infarction, CHD Mortality, and Case-Fatality in Four US Communities, 1987 to 2008. Circulation. 2012; 125: 1848-1857.
16. De Backer G, Ambrosioni E, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: third joint task force of European and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of eight societies and by invited experts).
Eur J Cardiovasc Prev Rehabil. 2003; 10: S1-10.
17. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014; 12: 1495-1499.
18. Geleijnse JM, Giltay EJ, Schouten EG, et al. Effect of low doses of n-3 fatty acids on cardiovascular diseases in 4,837 post-myocardial infarction patients: design and baseline characteristics of the Alpha Omega Trial. Am Heart J. 2010; 159: 539-546. 19. Giltay EJ, Geleijnse JM, Schouten EG, et al. High stability of markers of cardiovascular
risk in blood samples. Clin Chem. 2003; 49: 652-655.
20. Hoogeveen EK, Geleijnse JM, Kromhout D, et al. No effect of n-3 fatty acids on high-sensitivity C-reactive protein after myocardial infarction: the Alpha Omega Trial.
Eur J Prev Cardiol. 2014; 21: 1429-1436.
21. Mancia G, Fagard R, Narkiewicz K, et al. 2013 ESH/ESC Guidelines for the management of arterial hypertensionThe Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013; 34: 2159-2219.
22. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18: 499-502.
23. Catapano AL, Graham I, De Backer G, et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. Eur Heart J. 2016; 37: 2999-3058.
24. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. World Health Organ Tech Rep Ser. 2000: 894: 839-68. 25. Hoogeveen EK, Geleijnse JM, Kromhout D, et al. Effect of omega-3 fatty acids on
kidney function after myocardial infarction: the Alpha Omega Trial. Clin J Am Soc
Nephrol. 2014; 9: 1676-1683.
26. Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012; 367: 20-29.
27. Rifkin DE, Shlipak MG, Katz R, et al. Rapid kidney function decline and mortality risk in older adults. Arch Intern Med. 2008; 168: 2212-2218.
28. KDIGO. Kidney Disease - Improving Global Outcomes: KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney
Int. 2013; 3: 10.
29. Glymour MM, Weuve J, Berkman LF, et al. When Is Baseline Adjustment Useful in Analyses of Change? An Example with Education and Cognitive Change. Am J
Epidemiol. 2005; 162: 267-278.
30. Hayward RA, Krumholz HM. Three reasons to abandon low-density lipoprotein targets: an open letter to the Adult Treatment Panel IV of the National Institutes of Health. Circ Cardiovasc Qual Outcomes. 2012; 5: 2-5.
31. Piepoli MF, Hoes AW, Agewall S, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016; 37: 2315-2381. 32. Shlipak MG, Katz R, Kestenbaum B, et al. Rate of Kidney Function Decline in Older
Adults: A Comparison Using Creatinine and Cystatin C. Am J Nephrol. 2009; 30: 171-178.
33. Turin TC, Jun M, James MT, et al. Magnitude of rate of change in kidney function and future risk of cardiovascular events. Int J Cardiol. 2016; 202: 657-665.
34. Herber-Gast GM, Biesbroek S, Verschuren WM, et al. Association of dietary protein and dairy intakes and change in renal function: results from the population-based longitudinal Doetinchem cohort study. Am J Clin Nutr. 2016; 104: 1712-1719.
35. Salkind N. Encyclopedia of Research Design. Chapter: Volunteer Bias, Thousand Oaks, California: SAGE Publications, Inc.; 2010: 1609-1610.
36. Antman EM, Anbe DT, Armstrong PW, et al. ACC/AHA Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction. J Am Coll Cardiol. 2004; 44: E1-211.
37. Ryan TJ, Anderson JL, Antman EM, et al. ACC/AHA guidelines for the management of patients with acute myocardial infarction. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Management of Acute Myocardial Infarction). J Am Coll Cardiol. 1996; 28: 1328-1428.
38. Ritz E, Rychlík I, Locatelli F, et al. End-stage renal failure in type 2 diabetes: A medical catastrophe of worldwide dimensions. Am J Kidney Dis. 1999; 34: 795-808. 39. Hill GS. Hypertensive nephrosclerosis. Curr Opin Nephrol Hypertens. 2008; 17: 266-270. 40. Wanner C, Tonelli M. KDIGO Clinical Practice Guideline for Lipid Management in
CKD: summary of recommendation statements and clinical approach to the patient.
Kidney Int. 2014; 85: 1303-1309.
41. Stone NJ, Robinson JG, Lichtenstein AH, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014; 129: S1-45.
42. Zhou Q, Liao JK. Pleiotropic effects of statins. - Basic research and clinical perspectives. Circ J. 2010; 74: 818-826.
44. Elihimas Junior UF, Elihimas HC, Lemos VM, et al. Smoking as risk factor for chronic kidney disease: systematic review. J Bras Nefrol. 2014; 36: 519-528.
45. Shlipak MG, Matsushita K, Arnlov J, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013; 369: 932-943.
Table S2: Odds ratios (95%-CI) for risk of rapid creatinine cystatin C-based eGFR decline (>3 mL/min/1.73m2 per year) in 2344 post-MI patients, for different cardiovascular risk
factors.
Risk factor ¶ Crude Model 1 Model 2
Diabetes 1.28 (1.04; 1.59)* 1.27 (1.02; 1.58)* 1.24 (0.99; 1.54)
High blood pressure 1.30 (1.09; 1.54)* 1.28 (1.07; 1.52)* 1.31 (1.10; 1.56)*
High LDL 0.81 (0.68; 0.96)* 0.81 (0.68; 0.96)* 0.80 (0.67; 0.95)*
Current cigarette smoking 0.90 (0.71; 1.14) 0.93 (0.73; 1.18) 0.92 (0.72; 1.17) Obesity 1.07 (0.87; 1.31) 1.06 (0.86; 1.31) 1.05 (0.85; 1.29)
CI, confidence interval; LDL, low-density lipoprotein, eGFR, estimated glomerular filtration rate. * p<0.05
¶ Reference: absence of the risk factor of interest.
Diabetes was defined as self-reported diagnosis by a physician, use of glucose-lowering drugs, or hyperglycemia. High blood pressure was defined as systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure of ≥90 mmHg, irrespective of use of blood pressure lowering drugs. High
LDL was defined as serum LDL level ≥2.5 mmol/L. Obesity was defined as BMI ≥30.0 kg/m2.
Model 1: adjusted for age, sex and treatment group.
Model 2: Model 1 plus additional adjustment for current smoking, alcohol consumption, level of education, diabetes, high blood pressure, high LDL, and obesity. Analyses for obesity were not adjusted for diabetes, high blood pressure, and high LDL.
Figure S1: Flow chart of 2426 post-MI patients included in the present study. The patients
randomized before August 2005 are considered a random sample of the total population of 4837 patients. Of all deceased patients, 3 died of renal failure.
